Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

Background: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods: Two ICU databases were employed: eICU Collaborative Research Da...

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Bibliographic Details
Main Authors:	Gong, X. (Author), Yao, S. (Author), Zeng, Z. (Author), Zheng, J. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	adolescent adult age aged albumin Article body mass calibration cohort analysis controlled study diagnostic test accuracy study eICU-CRD ethnicity female fraction of inspired oxygen gender hospital admission hospital mortality Hospital mortality prediction human intensive care unit Intensive care unit lactic acid logistic regression analysis machine learning Machine learning major clinical study male MIMIC-III prediction random forest receiver operating characteristic retrospective study sepsis Sepsis
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View Fulltext in Publisher

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

Internet

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